import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm
		import torch_xla.debug.metrics as met




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def __init__(  self ,  *lowerCAmelCase__ ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  **lowerCAmelCase__		)	->   List[Any]:

							'''simple docstring'''


							super().__init__(*lowerCAmelCase__ ,  **lowerCAmelCase__		)
							__lowercase					     =				eval_examples
							__lowercase					     =				post_process_function





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__ = "eval"		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				self.eval_dataset if eval_dataset is None else eval_dataset
							__lowercase					     =				self.get_eval_dataloader(lowerCAmelCase__		)
							__lowercase					     =				self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							__lowercase					     =				self.compute_metrics
							__lowercase					     =				None
							__lowercase					     =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							__lowercase					     =				time.time()
							try:
											__lowercase					     =				eval_loop(
											    lowerCAmelCase__ ,  description='''Evaluation''' ,  prediction_loss_only=True if compute_metrics is None else None ,  ignore_keys=lowerCAmelCase__ ,  metric_key_prefix=lowerCAmelCase__ ,  )
							finally:
											__lowercase					     =				compute_metrics
							__lowercase					     =				self.args.eval_batch_size * self.args.world_size
							if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
											start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
							output.metrics.update(
							    speed_metrics(
							        lowerCAmelCase__ ,  lowerCAmelCase__ ,  num_samples=output.num_samples ,  num_steps=math.ceil(output.num_samples / total_batch_size		) ,  )		)
							if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
											# Only the main node write the results by default
											__lowercase					     =				self.post_process_function(lowerCAmelCase__ ,  lowerCAmelCase__ ,  output.predictions		)
											__lowercase					     =				self.compute_metrics(lowerCAmelCase__		)

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()		):
															if not key.startswith(F"{metric_key_prefix}_"		):
																			__lowercase					     =				metrics.pop(lowerCAmelCase__		)
											metrics.update(output.metrics		)
							else:
											__lowercase					     =				output.metrics

							if self.args.should_log:
											# Only the main node log the results by default
											self.log(lowerCAmelCase__		)

							if self.args.tpu_metrics_debug or self.args.debug:
											# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
											xm.master_print(met.metrics_report()		)

							__lowercase					     =				self.callback_handler.on_evaluate(self.args ,  self.state ,  self.control ,  lowerCAmelCase__		)
							return metrics





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__=None ,  lowerCAmelCase__ = "test"		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				self.get_test_dataloader(lowerCAmelCase__		)

							# Temporarily disable metric computation, we will do it in the loop here.
							__lowercase					     =				self.compute_metrics
							__lowercase					     =				None
							__lowercase					     =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							__lowercase					     =				time.time()
							try:
											__lowercase					     =				eval_loop(
											    lowerCAmelCase__ ,  description='''Prediction''' ,  prediction_loss_only=True if compute_metrics is None else None ,  ignore_keys=lowerCAmelCase__ ,  metric_key_prefix=lowerCAmelCase__ ,  )
							finally:
											__lowercase					     =				compute_metrics
							__lowercase					     =				self.args.eval_batch_size * self.args.world_size
							if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
											start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
							output.metrics.update(
							    speed_metrics(
							        lowerCAmelCase__ ,  lowerCAmelCase__ ,  num_samples=output.num_samples ,  num_steps=math.ceil(output.num_samples / total_batch_size		) ,  )		)

							if self.post_process_function is None or self.compute_metrics is None:
											return output

							__lowercase					     =				self.post_process_function(lowerCAmelCase__ ,  lowerCAmelCase__ ,  output.predictions ,  '''predict'''		)
							__lowercase					     =				self.compute_metrics(lowerCAmelCase__		)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()		):
											if not key.startswith(F"{metric_key_prefix}_"		):
															__lowercase					     =				metrics.pop(lowerCAmelCase__		)
							metrics.update(output.metrics		)
							return PredictionOutput(predictions=predictions.predictions ,  label_ids=predictions.label_ids ,  metrics=lowerCAmelCase__		)
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)